env-and-assets-bootstrap
Rigor Setup skill for README-first deep learning repo reproduction. Use when the task is specifically to prepare a conservative conda-first environment, checkpoint and dataset path assumptions, cache location hints, and setup notes before any run on a README-documented repository. Do not use for repo scanning, full orchestration, paper interpretation, final run reporting, or generic environment setup that is not tied to a specific reproduction target.
How do I install this agent skill?
npx skills add https://github.com/lllllllama/ai-paper-reproduction-skill --skill env-and-assets-bootstrapIs this agent skill safe to install?
- Gen Agent Trust Hubpass
The skill automates environment setup and asset preparation for AI research repositories. It uses standard tools like Conda and Pip to isolate dependencies. While the implementation follows security best practices (such as list-based subprocess calls), the skill inherently involves risks like remote code execution and indirect prompt injection because it installs software from and ingests data from untrusted repositories as part of its primary function.
- Socketpass
No alerts
- Snykwarn
Risk: MEDIUM · 1 issue
- ZeroLeakspass
Score: 93/100 · 2 sections analyzed
What does this agent skill do?
env-and-assets-bootstrap
Use this as the Rigor Setup skill. The installed slug remains
env-and-assets-bootstrap for compatibility.
Use the shared operating principles in
../../references/agent-operating-principles.md; this skill should keep setup
planning conservative while leaving environment-specific judgment to the model.
When to apply
- After repo intake identifies a credible reproduction target.
- When environment creation or asset path preparation is needed before running commands.
- When the repo depends on checkpoints, datasets, or cache directories.
- When the user explicitly wants setup help before any run attempt.
When not to apply
- When the repository already ships a ready-to-run environment that does not need translation.
- When the task is only to scan and plan.
- When the task is only to report results from commands that already ran.
- When the request is a generic conda or package-management question outside repo reproduction.
Clear boundaries
- This skill prepares environment and asset assumptions.
- It does not own target selection.
- It does not own final reporting.
- It does not perform paper lookup except by forwarding gaps to the optional paper resolver.
Input expectations
- target repo path
- selected reproduction goal
- relevant README setup steps
- any known OS or package constraints
Output expectations
- conservative environment setup notes
- candidate conda commands
- asset path plan
- checkpoint and dataset source hints
- unresolved dependency or asset risks
Notes
Use references/env-policy.md, references/assets-policy.md, scripts/bootstrap_env.py, scripts/plan_setup.py, and scripts/prepare_assets.py.
Use scripts/bootstrap_env.sh only as a POSIX wrapper around the Python bootstrapper when a shell entrypoint is more convenient.
How can the creator link this skill?
Add the canonical catalog link to the repository README so users can inspect current installs and available audits. The publishing guide covers the complete discovery path.
<a href="https://skillzs.dev/skills/lllllllama/ai-paper-reproduction-skill/env-and-assets-bootstrap">View env-and-assets-bootstrap on skillZs</a>